Why should anyone care about Evals?
summary
Evals are positioned as more than unit tests: Braintrust builds a dev platform that runs offline experiments to catch faults before deployment, lets teams tweak prompts in a playground, log outputs for observability, and prioritize real-world training data by applying the same metrics from offline to production. The concrete claim is that good evals handle 90% of the product iteration loop before shipping, creating a connected data flywheel that speeds releases and reduces risk.
AI-written summary · about
chapters (5)
- 0:00Introduction and journey
- 1:38The necessity of evals
- 2:53Benefits of robust testing
- 3:44Industry expert endorsement
- 4:18Evaluation platform overview
related talks
38mBuild Evals That Actually Matter
19mThe maturity phases of running evals
15mMalleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests?
31mBeyond Vibes: Building Evals for Generative AI
47mBuild Trustworthy AI Agents powered by Evals
19mEvals Are Broken, Use Them Anyway
06mThe Future of Evals: From LLM as a Judge to Agent as a Judge
45mBuild Your Own Benchmark: Why Agents Need Custom Evals and How to Create Them
